Like most deep learning (DL)-based tasks, limited data resources is always a challenge for GAN-based medical synthesis. In addition, large, diverse, and representative dataset is required to develop and refine best practices in evidence-based medicine. However, there is no single approach for generating synthetic data that is adaptive for all populations [1]}. Data collaboration between different medical institutions (of the heterogeneity of phenotypes in the gender, ethnicity, and geography of the individuals or patients, as well as in the healthcare systems, workflows, and equipment used) makes effects to build a robust model that can learn from diverse populations. But data sharing for data collection will cause data privacy problems which could be a risk of exposing patient information [2]}, [3]}. Federated learning (FL) [4]} is a privacy-preserving tool, which keeps data on each medical institute (clients) locally and exchanges model weights with the server to learn a global model collaboratively. As no data sharing is required, it is a popular research option in healthcare [5]}, [6]}. Federated GAN (FedGAN) is then proposed to train GAN distributively for data synthesis [7]}, [8]}. Its overall robustness against attacks is under explored.
